def __call__(self, candidate: BrainModel):
candidate.start_recording('IT', time_bins=self._time_bins)
stimulus_set = place_on_screen(
self._assembly.stimulus_set,
target_visual_degrees=candidate.visual_degrees(),
source_visual_degrees=self._visual_degrees)
# Temporal recordings from large candidates take up a lot of memory and compute time.
# In order to quickly reject recordings that are static over time,
# we will show one image and check whether the recordings vary over time at all or not.
# If they don't we can quickly score the candidate with a failure state
# since it will not be able to predict temporal differences with the OST metric
check_stimulus_set = stimulus_set[:1]
check_stimulus_set.identifier = None # unset identifier to avoid storing (interferes with actual stimulus_set)
check_recordings = candidate.look_at(
check_stimulus_set, number_of_trials=self._number_of_trials)
if not temporally_varying(check_recordings):
score = Score([np.nan, np.nan],
coords={'aggregation': ['center', 'error']},
dims=['aggregation'])
else:
recordings = candidate.look_at(
stimulus_set, number_of_trials=self._number_of_trials)
score = self._similarity_metric(recordings, self._assembly)
score = ceil_score(score, self.ceiling)
return score
def __call__(self, model: BrainModel):
model_identifier = model.identifier
model.start_recording(self.region, time_bins=self.timebins)
stim_pos = get_stimulus_position(self._assembly.stimulus_set)
in_rf = filter_receptive_fields(model_identifier=model_identifier,
model=model,
region=self.region,
pos=stim_pos)
responses = get_firing_rates(
model_identifier=model_identifier,
model=model,
region=self.region,
stimulus_identifier=self._assembly.stimulus_set.identifier,
number_of_trials=self._number_of_trials,
in_rf=in_rf)
baseline = get_firing_rates(model_identifier=model_identifier,
model=model,
region=self.region,
stimulus_identifier=BLANK_STIM_NAME,
number_of_trials=self._number_of_trials,
in_rf=in_rf)
model_property = self._neuronal_property(
model_identifier=model_identifier,
responses=responses,
baseline=baseline)
raw_score = self._similarity_metric(model_property, self._assembly)
ceiling = self._ceiling_func(self._assembly)
return ceil_score(raw_score, ceiling)
def __call__(self, candidate: BrainModel):
time_bins = [(time_bin_start, time_bin_start + 10)
for time_bin_start in range(70, 250, 10)]
candidate.start_recording('IT', time_bins=time_bins)
recordings = candidate.look_at(self._assembly.stimulus_set)
score = self._similarity_metric(recordings, self._assembly)
score = ceil_score(score, self.ceiling)
return score
def __call__(self, candidate: BrainModel):
time_bins = [(time_bin_start, time_bin_start + 10) for time_bin_start in range(70, 250, 10)]
candidate.start_recording('IT', time_bins=time_bins)
stimulus_set = place_on_screen(self._assembly.stimulus_set, target_visual_degrees=candidate.visual_degrees(),
source_visual_degrees=self._visual_degrees)
recordings = candidate.look_at(stimulus_set)
score = self._similarity_metric(recordings, self._assembly)
score = ceil_score(score, self.ceiling)
return score
def __call__(self, candidate: BrainModel):
candidate.start_recording(self.region, time_bins=self.timebins)
stimulus_set = place_on_screen(self._assembly.stimulus_set, target_visual_degrees=candidate.visual_degrees(),
source_visual_degrees=self._visual_degrees)
source_assembly = candidate.look_at(stimulus_set, number_of_trials=self._number_of_trials)
if 'time_bin' in source_assembly.dims:
source_assembly = source_assembly.squeeze('time_bin') # static case for these benchmarks
raw_score = self._similarity_metric(source_assembly, self._assembly)
return explained_variance(raw_score, self.ceiling)
def __call__(self, candidate: BrainModel, do_behavior=False):
# Do brain region task
candidate.start_recording(self.region, time_bins=self.timebins)
source_assembly = candidate.look_at(self.assembly.stimulus_set)
# Do behavior task
if do_behavior:
candidate.start_task(BrainModel.Task.probabilities,
self.assembly.stimulus_set)
candidate.look_at(self.assembly.stimulus_set)
raw_score = self._similarity_metric(source_assembly, self.assembly)
return ceil_score(raw_score, self.ceiling)
def __call__(self, candidate: BrainModel, do_behavior=False):
# Check neural recordings
candidate.start_recording(self.region, time_bins=self.timebins)
source_assembly = candidate.look_at(self.assembly.stimulus_set)
# Check behavioral tasks
if do_behavior:
candidate.start_task(BrainModel.Task.probabilities,
self.assembly.stimulus_set)
candidate.look_at(self.assembly.stimulus_set)
candidate.start_task(BrainModel.Task.label, 'imagenet')
candidate.look_at(self.assembly.stimulus_set)
raw_score = self._similarity_metric(source_assembly, self.assembly)
return ceil_score(raw_score, self.ceiling)